This is one of my subjects in study and fortunately my strongest one. ANN is kinda general, what are you planning, back propagation? kohonen?

to your questions, IMO1. yes it can2. the more hidden layers you have, the adaptive/learning skill will be better. For example if you use it to recognize pattern, it can spot minor details. It can also reduce error margin between literation.3. college nobody want to read calculus books at home *.

This is one of my subjects in study and fortunately my strongest one. ANN is kinda general, what are you planning, back propagation? kohonen?

to your questions, IMO1. yes it can2. the more hidden layers you have, the adaptive/learning skill will be better. For example if you use it to recognize pattern, it can spot minor details. It can also reduce error margin between literation.3. college nobody want to read calculus books at home *.

*) applied to common people, especially non gamer ones.

For one, I'm glad to have someone who's experienced in this, because I need a lot of help! Hahah.

Anyway, I need it to recognize mostly photos, as I'm working on an adapative AI. Essentially it will be trained to recognize people and things, along with text. Even person/thing will be marked with a good or bad meter so it knows how to react to certain stimuli and so on. I've chosen to give it "eyes" because I plan on welding some parts and making a nifty little robot arm or something for fun over the summer.

But yeah, I need it to recognize places and things. Problem is, I have no real idea on how to, and a lot of the examples are written so mathematically I struggle to comprehend a lot of it. I wanted to use backpropagation because it seemed best for allowing it to learn by itself in some cases.

Back propagation is best use on prediction or data mining. For pattern like you saud, you may need hopfield. Actually, rather make one by yourself there's already Neuroph library which quite powerful and you'll have a working network by less than 20 lines of code

Back propagation is best use on prediction or data mining. For pattern like you saud, you may need hopfield. Actually, rather make one by yourself there's already Neuroph library which quite powerful and you'll have a working network by less than 20 lines of code

It's a lot cooler to do it yourself. I thought hopfields were slow learners?

I have no problem in PM'ing/copying, the problem is translating. They're not written in English

If you could translate the important stuff and send them to me I'd be thankful. If you write in a language with a relatively similar alphabet to english I can translate them myself though. I'd be quite thankful!

One thing is that it looks like the number of nodes in your hidden layer(s) are the same as in your input layer. Two nodes probably won't be enough to represent the XOR function - try 3 or more.

Also, I've never heard of any value in more than two hidden layers and I'm pretty sure that theoretically two is sufficient for any mapping from inputs to outputs (though the layers might have to be large in some cases).

ANN is a specialized case of non-linear optimization, which is a very tricky area, filled with black art tricks.

Also, there is often a better alternative available for the problem than NNs, if you can figure out good features (there are some really good features for images that you can use) and precalculate them and use as much linear optimization as possible etc.

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